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Record W2900641910 · doi:10.1002/esp.4549

Recording inter‐annual changes on a boulder beach in Galicia, NW Spain using an unmanned aerial vehicle

2018· article· en· W2900641910 on OpenAlexaff
Alejandro Gómez‐Pazo, Augusto Pérez Albertí, Alan S. Trenhaile

Bibliographic record

VenueEarth Surface Processes and Landforms · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsUniversity of Windsor
FundersMinisterio de Educación, Cultura y Deporte
KeywordsDeposition (geology)StormErosionAccretion (finance)GeologyCoastal erosionDigital elevation modelPhysical geographyHigh resolutionAerial photosOceanographyHydrology (agriculture)Environmental scienceGeomorphologyGeographyRemote sensingSediment

Abstract

fetched live from OpenAlex

Abstract An unmanned aerial vehicle (UAV) was flown over a boulder beach (area 20 000 m 2 ) on the southern coast of Galicia (northwestern Spain) in May 2016, continuing earlier work based on flights over the same beach in July 2012, May 2013, and late March 2014. Digital surface models (DSMs) with 1.8 cm resolution were constructed from the 2014 and 2016 data to identify changes in beach morphology over the intervening period. Analyses were conducted using a Limit of Detection (LoD) of 0 cm and 3.71 cm. In both cases, the analyses showed that erosion dominated over 19% of the beach area. Accretion occurred over the rest of the beach, which acquired an additional 1500 m 3 of material over the study period. Re‐analysis of the data from earlier flights suggested that erosion dominated on the beach in 2012–2013 and deposition in 2013–2014. Without any clear relationship between beach behaviour and storm severity during each winter period, it is proposed that gravitationally induced erosion and storm‐wave induced deposition are the result of perturbations about an equilibrium beach gradient. The UAV data also suggested that an essentially random component modulates regional patterns of movement. © 2018 John Wiley & Sons, Ltd.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.240
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations24
Published2018
Admission routes1
Has abstractyes

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